Virome-wide serological profiling reveals association of herpesviruses with obesity
Bibliographic record
Abstract
The relationship between viral infection and obesity has been known for several decades but epidemiological data is limited to only a few viral pathogens. The association between obesity and a wide range of viruses was assessed using VirScan, a pan-viral serological profiling tool. Serum specimens from 457 Qatari adults (lean = 184; obese = 273) and 231 Qatari children (lean = 111; obese = 120) were analyzed by VirScan. Associations with obesity were determined by odds ratio (OR) and Fisher's test (p values), and by multivariate regression analysis to adjust for age and gender. Although there was no association of viral infections with obesity in the pediatric population, a nominal association of obesity with seropositivity to members of the Herpesviridae family is observed for the adult population (OR = 1.5-3.3; p < 0.05). After adjusting p values for multiple comparisons (Bonferroni correction) the odds of being obese is significantly higher in herpes simplex virus 1 (HSV-1) seropositive Qatari adults (OR = 3.3; 95% CI 2.15-4.99; p = 2.787E - 08). By VirScan, the sero-prevalence of HSV1 is 81.3% and 57.1% among Qatari obese and lean adult populations, respectively. Higher prevalence of antibodies against several peptide epitopes of HSV-1/2 is positively associated with obesity (OR = 2.35-3.82; p ≤ 3.981E - 05). By multivariate regression analysis, HSV-1 was independently associated with obesity irrespective of age and gender. Our results suggest that obesity among Qataris may be associated with a higher prevalence of herpesvirus infections, in particular HSV-1. Furthermore, the high prevalence of antibodies against peptide antigens specific to HSV-1 and -2 in the obese population suggests that these viral peptides may play a role in adipogenesis. Further studies with these candidate peptides in cell culture or animal models may confirm their adipogenic roles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".